The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructi
ve quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may pro
vide
viable alternati
ves and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy pro
vides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructi
ve soybean measurements. We show that TRS uses the light scattering properties of soybeans to effecti
vely homogenize the heterogeneous bulk of a soybean for representati
ve sampling. Working with o
ver 1000 indi
vidual intact soybean seeds, we de
veloped a simple partial least-squares model for predicting oil and protein content nondestructi
vely. We find TRS to ha
ve a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and
validation sets, the predicati
ve capabilities of the model were similar to the error in the reference methods.
Keywords:
soybean; ve+analysis&qsSearchArea=searchText">nondestructive analysis; protein; oil; Raman spectroscopy; transmission; light scattering